Mohamed–Chaker Larabi

252 total papers · 2.0k total citations
79 papers, 489 citations indexed

About

Mohamed–Chaker Larabi is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing. According to data from OpenAlex, Mohamed–Chaker Larabi has authored 79 papers receiving a total of 489 indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Computer Vision and Pattern Recognition, 22 papers in Media Technology and 15 papers in Signal Processing. Recurrent topics in Mohamed–Chaker Larabi's work include Image and Video Quality Assessment (38 papers), Visual Attention and Saliency Detection (24 papers) and Advanced Vision and Imaging (19 papers). Mohamed–Chaker Larabi is often cited by papers focused on Image and Video Quality Assessment (38 papers), Visual Attention and Saliency Detection (24 papers) and Advanced Vision and Imaging (19 papers). Mohamed–Chaker Larabi collaborates with scholars based in France, Norway and Tunisia. Mohamed–Chaker Larabi's co-authors include Christine Fernández-Maloigne, Aldo Maalouf, Sid Ahmed Fezza, Libor Váša, Massimiliano Corsini, Guillaume Lavoué, Kai Wang, Faouzi Alaya Cheikh, Kamel Mohamed Faraoun and Tania Pouli and has published in prestigious journals such as IEEE Access, Sensors and Neurocomputing.

In The Last Decade

Mohamed–Chaker Larabi

74 papers receiving 480 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Mohamed–Chaker Larabi 419 173 88 56 54 79 489
Péter Tamás Kovács 296 0.7× 225 1.3× 113 1.3× 57 1.0× 46 0.9× 36 407
Ramakrishna Kakarala 358 0.9× 97 0.6× 57 0.6× 8 0.1× 62 1.1× 56 548
Luc J. Van Gool 392 0.9× 46 0.3× 35 0.4× 46 0.8× 36 0.7× 41 498
Kazunori Kotani 400 1.0× 120 0.7× 38 0.4× 8 0.1× 49 0.9× 77 542
Chang-Yeong Kim 223 0.5× 59 0.3× 68 0.8× 40 0.7× 25 0.5× 66 419
Ji‐Sang Yoo 276 0.7× 173 1.0× 18 0.2× 20 0.4× 34 0.6× 92 427
V. Javier Traver 233 0.6× 102 0.6× 46 0.5× 11 0.2× 20 0.4× 44 498
Heeseok Oh 335 0.8× 184 1.1× 96 1.1× 12 0.2× 21 0.4× 33 505
Lutz Goldmann 454 1.1× 131 0.8× 54 0.6× 30 0.5× 106 2.0× 41 505
Simon Heinzle 338 0.8× 141 0.8× 32 0.4× 123 2.2× 47 0.9× 25 458

Countries citing papers authored by Mohamed–Chaker Larabi

Since Specialization
Citations

This map shows the geographic impact of Mohamed–Chaker Larabi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohamed–Chaker Larabi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohamed–Chaker Larabi more than expected).

Fields of papers citing papers by Mohamed–Chaker Larabi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohamed–Chaker Larabi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohamed–Chaker Larabi. The network helps show where Mohamed–Chaker Larabi may publish in the future.

Co-authorship network of co-authors of Mohamed–Chaker Larabi

This figure shows the co-authorship network connecting the top 25 collaborators of Mohamed–Chaker Larabi. A scholar is included among the top collaborators of Mohamed–Chaker Larabi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohamed–Chaker Larabi. Mohamed–Chaker Larabi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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